Multi-image Super-resolution via Quality Map Associated Temporal Attention Network
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With the rising interest in deep learning-based methods in remote sensing, neural networks have made remarkable advancements in multi-image fusion and super-resolution. To fully exploit the advantages of multi-image super-resolution, temporal attention is crucial as it allows a model to focus on reliable features rather than noises. Despite the presence of quality maps (QMs) that indicate noises in images, most of the methods tested in the PROBA-V dataset have not been used QMs for temporal attention. We present a quality map associated temporal attention network (QA-Net), a novel method that incorporates QMs into both feature representation and fusion processes for the first time. Low-resolution features are temporally attended by QM features in repeated multi-head attention modules. The proposed method achieved state-of-the-art results in the PROBA-V dataset.
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